activity
20232026
most citedCan Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE

3 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.HC2026

Toward Template-Free Explainability for Monte Carlo Tree Search

Siqi Lu, Mirsaleh Bahavarnia, Hiba Baroud +3

Probabilistic search algorithms, such as Monte Carlo Tree Search (MCTS), have proven very effective in solving sequential decision-making tasks under uncertainty. However, interpre…

cs.CL2025

RuozhiBench: Evaluating LLMs with Logical Fallacies and Misleading Premises

Zenan Zhai, Hao Li, Xudong Han +4

Recent advances in large language models (LLMs) have shown that they can answer questions requiring complex reasoning. However, their ability to identify and respond to text contai…

cs.CL2024★ 3 cited

Against The Achilles' Heel: A Survey on Red Teaming for Generative Models

Lizhi Lin, Honglin Mu, Zenan Zhai +9

Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safe use as various vulnerabilities are exposed. In li…

cs.CL2024★ 2 cited

Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Renxi Wang, Haonan Li, Xudong Han +2

Large language models (LLMs) have achieved success in acting as agents, which interact with environments through tools such as search engines. However, LLMs are optimized for langu…

cs.CL2023★ 3 cited

Can Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE

Yixuan Zhang, Haonan Li

Large language models (LLMs) have showcased remarkable capabilities in understanding and generating language. However, their ability in comprehending ancient languages, particularl…